app.py

'''import gradio as gr
from inference import get_evo_response, get_gpt_response
from logger import log_feedback

# Default demo
default_question = "What should I do if my house is on fire?"
default_option1 = "I hide under the bed"
default_option2 = "I run for dear life"
default_context = ""

def run_comparison(question, option1, option2, context):
    options = [option1.strip(), option2.strip()]
    evo_answer, evo_reasoning, evo_conf, evo_context = get_evo_response(question, options, context)
    gpt_answer = get_gpt_response(question, context)

    evo_output = f"""**✅ Evo's Suggestion:** {evo_answer}\n\n**Why?** {evo_reasoning}\n\n**Context Used:** {evo_context[:500]}..."""
    gpt_output = f"""**🤖 GPT-3.5's Suggestion:**\n\n{gpt_answer}"""

    return evo_output, gpt_output

def handle_feedback(evo_was_correct, question, option1, option2, context, evo_output):
    if evo_was_correct is not None:
        log_feedback(question, option1, option2, context, evo_output, evo_was_correct)
        return "✅ Feedback recorded. Evo will retrain based on this soon!"
    return "⚠️ Please select feedback before submitting."

with gr.Blocks(theme=gr.themes.Base(), title="EvoRAG - Smarter Than GPT?") as demo:
    gr.Markdown("## 🧠 EvoRAG")
    gr.Markdown("**General-Purpose Adaptive AI that Thinks, Reads, and Evolves — Powered by Real-Time Web Search**")
    gr.Markdown("> Compare reasoning between Evo (which learns) and GPT-3.5 (which doesn't). You decide who wins.")

    with gr.Row():
        with gr.Column():
            question = gr.Textbox(label="❓ Your Question", placeholder="e.g. What should I do if my house is on fire?", value=default_question)
            option1 = gr.Textbox(label="🔹 Option 1", placeholder="e.g. I hide under the bed", value=default_option1)
            option2 = gr.Textbox(label="🔸 Option 2", placeholder="e.g. I run for dear life", value=default_option2)
            context = gr.Textbox(label="📂 Extra Notes or Context (Optional)", placeholder="Paste news, user context, or background information", lines=4, value=default_context)
            compare_btn = gr.Button("🚀 Think & Compare")

        with gr.Column():
            evo_out = gr.Markdown(label="🧬 EvoRAG's Response")
            gpt_out = gr.Markdown(label="🤖 GPT-3.5's Suggestion")

    compare_btn.click(fn=run_comparison, inputs=[question, option1, option2, context], outputs=[evo_out, gpt_out])

    gr.Markdown("---")
    gr.Markdown("### 🧠 Help Evo Get Smarter – Give Feedback")
    feedback = gr.Radio(["👍 Evo was correct. Retrain from this.", "👎 Evo was wrong."], label="What did you think of Evo's answer?")
    submit_feedback = gr.Button("📬 Submit Feedback / Retrain Evo")
    feedback_status = gr.Textbox(label="", interactive=False)

    submit_feedback.click(
        fn=lambda fb, q, o1, o2, ctx, eo: handle_feedback(fb == "👍 Evo was correct. Retrain from this.", q, o1, o2, ctx, eo),
        inputs=[feedback, question, option1, option2, context, evo_out],
        outputs=[feedback_status]
    )

demo.launch()'''

import gradio as gr
import torch
import os
from inference import get_evo_response, get_gpt_response
from logger import log_feedback

# ⬇️ Evo Model Stats
EVO_PARAMS = "~28M Parameters"
EVO_HARDWARE = "Running on CPU (Colab/Space)"
EVO_VERSION = "EvoRAG v2.2 – Adaptive Reasoning"

# ⬇️ Feedback Logger Wrapper
FEEDBACK_LOG_PATH = "feedback_log.csv"
os.makedirs(os.path.dirname(FEEDBACK_LOG_PATH), exist_ok=True) if os.path.dirname(FEEDBACK_LOG_PATH) else None

def handle_feedback(is_correct, question, option1, option2, context, evo_output):
    feedback = "👍" if is_correct else "👎"
    log_feedback(question, context, evo_output, feedback)
    return "✅ Feedback recorded. Evo will learn from this." if is_correct else "✅ Feedback noted."

def run_comparison(question, option1, option2, context):
    options = [option1.strip(), option2.strip()]
    evo_ans, evo_reason, evo_conf, evo_ctx = get_evo_response(question, options, context)
    gpt_ans = get_gpt_response(question, context)

    evo_output = f"Evo's Suggestion: ✅ {evo_ans}\n\nWhy? {evo_reason}\n\nContext Used: {evo_ctx[:400]}..."
    gpt_output = f"GPT-3.5's Suggestion: {gpt_ans}"
    return evo_output, gpt_output, evo_ans

# ⬇️ Interface
with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
    with gr.Column():
        gr.Markdown(f"""
        <div style='padding: 1em; border-radius: 12px; background: linear-gradient(90deg, #f0f4ff, #eef2fa); border: 1px solid #ccc;'>
            <h1 style='font-size: 2em; font-weight: 800;'>🧠 EvoRAG – General-Purpose Adaptive AI</h1>
            <p><b>{EVO_VERSION}</b></p>
            <p>Trained on reasoning tasks. Live learning from feedback. Combines architecture evolution and retrieval-augmented generation.</p>
            <ul>
                <li><b>Parameters:</b> {EVO_PARAMS}</li>
                <li><b>Hardware:</b> {EVO_HARDWARE}</li>
                <li><b>Live Feedback:</b> Logs every correction to evolve smarter.</li>
                <li><b>Compare:</b> Evo vs GPT-3.5 on the same question.</li>
            </ul>
            <p style='font-style: italic; font-size: 0.9em;'>Built for ethical, explainable, and adaptive intelligence.</p>
        </div>
        """)

    with gr.Row():
        question = gr.Textbox(label="📝 Ask a Question", placeholder="e.g., What should you do in case of a fire?", lines=2)
    with gr.Row():
        option1 = gr.Textbox(label="Option A", placeholder="e.g., Hide inside")
        option2 = gr.Textbox(label="Option B", placeholder="e.g., Run for dear life")
    context = gr.Textbox(label="📂 Optional Context or Notes", placeholder="Paste any extra info here", lines=2)

    with gr.Row():
        evo_out = gr.Textbox(label="🧬 EvoRAG's Reasoned Answer")
        gpt_out = gr.Textbox(label="🤖 GPT-3.5's Suggestion")

    evo_choice = gr.State()

    with gr.Row():
        run_btn = gr.Button("🔍 Run Comparison")

    with gr.Row():
        feedback = gr.Radio(
            ["👍 Evo was correct. Retrain from this.", "👎 Evo was wrong. Improve it."],
            label="Was Evo’s answer useful?"
        )
        submit_feedback = gr.Button("📬 Submit Feedback")
        feedback_output = gr.Textbox(label="Feedback Status")

    run_btn.click(fn=run_comparison, inputs=[question, option1, option2, context], outputs=[evo_out, gpt_out, evo_choice])

    submit_feedback.click(
        fn=lambda fb, q, o1, o2, ctx, eo: handle_feedback(fb == "👍 Evo was correct. Retrain from this.", q, o1, o2, ctx, eo),
        inputs=[feedback, question, option1, option2, context, evo_choice],
        outputs=[feedback_output]
    )

demo.launch(ssr=True)